{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Plasma_glucose_concentration': 0, 'blood_pressure': 1, 'Triceps_skin_fold_thickness': 2, 'serum_insulin': 3, 'BMI': 4, 'Diabetes_pedigree_function': 5, 'Age': 6, 'index': 7}\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "dataSet = pd.read_csv('pima-indians-diabetes.csv')\n",
    "head = dataSet.head()\n",
    "dataSet['index'] = range(0,dataSet.shape[0])\n",
    "filter_func = lambda x : x != 'Target' and x!= 'pregnants'\n",
    "head = [ k for k in dataSet.columns.values if filter_func(k.strip()) ]\n",
    "\n",
    "columns_index = { k : i for (i,k) in enumerate(head)}\n",
    "print(columns_index )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据集基本信息 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataSet.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataSet.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  检查特征值无效情况"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plasma_glucose_concentration：口服葡萄糖耐量试验中2小时后的血浆葡萄糖浓度 \n",
    "blood_pressure：舒张压，单位:mm Hg \n",
    "Triceps_skin_fold_thickness：三头肌皮褶厚度，单位：mm \n",
    "serum_insulin：餐后血清胰岛素，单位:mm \n",
    "BMI：体重指数（体重（公斤）/ 身高（米）^2） \n",
    "Diabetes_pedigree_function：糖尿病家系作用 \n",
    "Age：年龄 \n",
    "以上几相为0值,都认为是无效数据.用直方图观察情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plasma_glucose_concentration 0\n",
      "blood_pressure 1\n",
      "Triceps_skin_fold_thickness 2\n",
      "serum_insulin 3\n",
      "BMI 4\n",
      "Diabetes_pedigree_function 5\n",
      "Age 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/seraph/anaconda3/lib/python3.7/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n",
      "Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  \"Adding an axes using the same arguments as a previous axes \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "f = plt.figure(figsize=(20,20))\n",
    "ax = plt.subplot(3,3,1)\n",
    "for k  in columns_index:\n",
    "    if k == 'index':\n",
    "        continue\n",
    "    print(k,columns_index[k])\n",
    "    ax = plt.subplot(3,3,1 + columns_index[k])\n",
    "    sns.distplot(dataSet[k],ax=ax)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由直方图可以看出,无效值在serum_insulin,Triceps_skin_fold_thickness两个特征上还是比较多的.看一下总数,以及取得无效值时候有多少在同一个样本上"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对serum_insulin,triceps_skin_fold_thickness两个特征用中值替代\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "118.66016303168442\n",
      "26.606479220920118 26.606479220920118\n"
     ]
    },
    {
     "data": {
      "image/png": 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M+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAgBVSVQeq6rNV9UBV3VdVb1vW31tVf1ZVdy+PKzZ85l1VdaKq/rSqXrV70wMAq2jPbg8AAMC2eizJO7r7y1X1C0m+VFV3LO9d392/tXFzVV2c5KokL03ywiT/uar+enc/vqNTAwAry5lPAAArpLtPdfeXl+ffS/JAkn1n+cjRJJ/o7ke7+2tJTiS5dH5SAOBcIT4BAKyoqjqY5JIkX1yW3lpV91TVh6vqwmVtX5IHN3zsZM4eqwAAfiLiEwDACqqq5yb5VJK3d/d3k9yQ5MVJDic5leT9Z7Zu8vHe5PuOVdXxqjp++vTpoakBgFUkPgEArJiqOj/r4elj3f3pJOnuh7v78e7+YZIP5YlL604mObDh4/uTPPTk7+zuG7v7SHcfWVtbm/0HAAArRXwCAFghVVVJbkryQHd/YMP63g3bXpvk3uX5bUmuqqpnV9WLkhxKctdOzQsArD6/dgcAsFpenuSNSb5SVXcva+9O8vqqOpz1S+q+nuQtSdLd91XVrUnuz/ov5V3rl+4AgO00Fp+q6sNJ/m6SR7r7l5e19yb5h0nO3Cjg3d19+/Leu5Jck+TxJP+0u/9wajYAgFXV3Z/P5vdxuv0sn7kuyXVjQwEA57TJy+4+muTVm6xf392Hl8eZ8HRxkquSvHT5zO9W1XmDswEAAACwA8biU3d/Lsk3t7j9aJJPdPej3f21JCfyxE0wAQAAAHiG2o0bjr+1qu6pqg9X1YXL2r4kD27Yc3JZAwAAAOAZbKfj0w1JXpzkcJJTSd6/rG92X4Le7Auq6lhVHa+q46dPn95sCwAAAAA/I3Y0PnX3w939eHf/MMmH8sSldSeTHNiwdX+Sh57iO27s7iPdfWRtbW12YAAAAACelh2NT1W1d8PL1ya5d3l+W5KrqurZVfWiJIeS3LWTswEAAACw/fZMfXFVfTzJK5JcVFUnk7wnySuq6nDWL6n7epK3JEl331dVtya5P8ljSa7t7senZgMAAABgZ4zFp+5+/SbLN51l/3VJrpuaBwAAAICdtxu/dgcAAADAOUJ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgAAAGCM+AQAAADAGPEJAAAAgDHiEwAAAABjxCcAAAAAxohPAAAAAIwRnwAAAAAYIz4BAAAAMEZ8AgBYIVV1oKo+W1UPVNV9VfW2Zf15VXVHVX11+Xvhsl5V9cGqOlFV91TVy3b3XwAArBrxCQBgtTyW5B3d/ZIklyW5tqouTvLOJHd296Ekdy6vk+Q1SQ4tj2NJbtj5kQGAVbal+FRVd25lDQCA7fHTHn9196nu/vLy/HtJHkiyL8nRJDcv225OcuXy/GiSW3rdF5JcUFV7t+GfAACQJNlztjer6jlJ/lKSi5ZTs2t56xeTvHB4NgCAc852Hn9V1cEklyT5YpIXdPepZD1QVdXzl237kjy44WMnl7VTP+U/AQDgR5w1PiV5S5K3Z/1A50t54uDnu0n+7eBcAADnqm05/qqq5yb5VJK3d/d3q+opt26y1pt837GsX5aXX/qlX9rqGAAAZ49P3f3bSX67qv5Jd//ODs0EAHDO2o7jr6o6P+vh6WPd/ell+eGq2ruc9bQ3ySPL+skkBzZ8fH+ShzaZ68YkNybJkSNHfixOAQA8lb/ozKckSXf/TlX9nSQHN36mu28ZmgsA4Jz20x5/1fopTjcleaC7P7DhrduSXJ3kfcvfz2xYf2tVfSLJryT5zpnL8wAAtsOW4lNV/V6SFye5O8njy3InEZ8AAAY8jeOvlyd5Y5KvVNXdy9q7sx6dbq2qa5J8I8nrlvduT3JFkhNJfpDkzdv1bwAASLYYn5IcSXJxdzvFGgBgZ/xUx1/d/flsfh+nJHnlJvs7ybU/+XgAAFvzrC3uuzfJX50cBACAH+H4CwBYCVs98+miJPdX1V1JHj2z2N2/NjIVAACOvwCAlbDV+PTeySEAAPgx793tAQAAtsNWf+3uv0wPAgDAExx/AQCrYqu/dve9rP+6SpL8XJLzk/zf7v7FqcEAAM5ljr8AgFWx1TOffmHj66q6MsmlIxMBAOD4CwBYGVv9tbsf0d3/Icnl2zwLAABPwfEXAPBMtdXL7n59w8tnJTmSJ04DBwBgmzn+AgBWxVZ/7e7vbXj+WJKvJzm67dMAAHCG4y8AYCVs9Z5Pb54eBACAJzj+AgBWxZbu+VRV+6vq96vqkap6uKo+VVX7p4cDADhXOf4CAFbFVm84/pEktyV5YZJ9Sf7jsgYAwAzHXwDASthqfFrr7o9092PL46NJ1gbnAgA41zn+AgBWwlbj059X1Ruq6rzl8YYk/2dyMACAc5zjLwBgJWw1Pv2DJH8/yf9OcirJbyRxE0wAgDmOvwCAlbClX7tL8q+SXN3d30qSqnpekt/K+kERAADbz/EXALAStnrm0988c+CTJN39zSSXzIwEAEAcfwEAK2Kr8elZVXXhmRfL/7xt9awpAAB+co6/AICVsNUDmPcn+a9V9ckknfX7D1w3NhUAAI6/AICVsKX41N23VNXxJJcnqSS/3t33j04GAHAOc/wFAKyKLZ+6vRzsOOABANghjr8AgFWw1Xs+AQAAAMBPTHwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMeITAAAAAGPEJwAAAADGiE8AAAAAjBGfAAAAABgjPgEAAAAwRnwCAAAAYIz4BAAAAMAY8QkAAACAMWPxqao+XFWPVNW9G9aeV1V3VNVXl78XLutVVR+sqhNVdU9VvWxqLgAAAAB2zuSZTx9N8uonrb0zyZ3dfSjJncvrJHlNkkPL41iSGwbnAgAAAGCHjMWn7v5ckm8+aflokpuX5zcnuXLD+i297gtJLqiqvVOzAQAAALAzdvqeTy/o7lNJsvx9/rK+L8mDG/adXNYAAAAAeAb7WbnheG2y1pturDpWVcer6vjp06eHxwIAAADg6djp+PTwmcvplr+PLOsnkxzYsG9/koc2+4LuvrG7j3T3kbW1tdFhAQAAAHh6djo+3Zbk6uX51Uk+s2H9Tcuv3l2W5DtnLs8DAAAA4Jlrz9QXV9XHk7wiyUVVdTLJe5K8L8mtVXVNkm8ked2y/fYkVyQ5keQHSd48NRcAAAAAO2csPnX365/irVdusreTXDs1CwAAAAC742flhuMAAAAArCDxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEAAAAwBjxCQAAAIAx4hMAAAAAY8QnAAAAAMaITwAAAACMEZ8AAAAAGCM+AQAAADBGfAIAAABgjPgEALBCqurDVfVIVd27Ye29VfVnVXX38rhiw3vvqqoTVfWnVfWq3ZkaAFhl4hMAwGr5aJJXb7J+fXcfXh63J0lVXZzkqiQvXT7zu1V13o5NCgCcE8QnAIAV0t2fS/LNLW4/muQT3f1od38tyYkkl44NBwCck8QnAIBzw1ur6p7lsrwLl7V9SR7csOfksgYAsG3EJwCA1XdDkhcnOZzkVJL3L+u1yd7e7Auq6lhVHa+q46dPn56ZEgBYSeITAMCK6+6Hu/vx7v5hkg/liUvrTiY5sGHr/iQPPcV33NjdR7r7yNra2uzAAMBKEZ8AAFZcVe3d8PK1Sc78Et5tSa6qqmdX1YuSHEpy107PBwCstj27PQAAANunqj6e5BVJLqqqk0nek+QVVXU465fUfT2pNloLAAAgAElEQVTJW5Kku++rqluT3J/ksSTXdvfjuzE3ALC6xCcAgBXS3a/fZPmms+y/Lsl1cxMBAOc6l93x/9m782hNqvJe/N+im0HAgaEZFBxiNIljYgjXIXqj3sgsQ+MYE+IQ4pxoTGJ+mpu4ktxlNLlGHIMjxtluGhBwuibGxCjOKMYBBGRo6G4ElRm6u35/7L27qg/vOUxd3dh8PmuddepUPe+uXbVrfGrXewAAAAAmI/kEAAAAwGQknwAAAACYjOQTAAAAAJORfAIAAABgMpJPAAAAAExG8gkAAACAyUg+AQAAADAZyScAAAAAJiP5BAAAAMBkJJ8AAAAAmIzkEwAAAACTkXwCAAAAYDKSTwAAAABMRvIJAAAAgMlIPgEAAAAwGcknAAAAACYj+QQAAADAZCSfAAAAAJiM5BMAAAAAk5F8AgAAAGAykk8AAAAATEbyCQAAAIDJSD4BAAAAMBnJJwAAAAAmI/kEAAAAwGQknwAAAACYjOQTAAAAAJORfAIAAABgMpJPAAAAAExG8gkAAACAyUg+AQAAADAZyScAAAAAJiP5BAAAAMBkJJ8AAAAAmIzkEwAAAACTkXwCAAAAYDKSTwAAAABMRvIJAAAAgMlIPgEAAAAwGcknAAAAACYj+QQAAADAZCSfAAAAAJiM5BMAAAAAk5F8AgAAAGAykk8AAAAATEbyCQAAAIDJSD4BAAAAMBnJJwAAAAAmI/kEAAAAwGQknwAAAACYjOQTAAAAAJORfAIAAABgMpJPAAAAAExG8gkAAACAyUg+AQAAADAZyScAAAAAJiP5BAAAAMBkJJ8AAAAAmIzkEwAAAACTkXwCAAAAYDKSTwAAAABMRvIJAAAAgMlIPgEAAAAwGcknAAAAACYj+QQAAADAZCSfAAAAAJiM5BMAAAAAk5F8AgAAAGAykk8AAAAATEbyCQAAAIDJSD4BAAAAMBnJJwAAAAAmI/kEAAAAwGQknwAAAACYjOQTAAAAAJORfAIAAABgMpJPAAAAAExG8gkAAACAyUg+AQAAADAZyScAAAAAJiP5BAAAAMBkJJ8AAAAAmIzkEwAAAACTkXwCAAAAYDKSTwAAAABMRvIJAAAAgMlIPgEAAAAwGcknAAAAACYj+QQAAADAZCSfAAAAAJiM5BMAAAAAk5F8AgAAAGAykk8AAAAATEbyCQAAAIDJSD4BAAAAMBnJJwAAAAAmI/kEAAAAwGQknwAAAACYjOQTAAAAAJORfAIAAABgMpJPAAAAAExG8gkAAACAyUg+AQAAADAZyScAAAAAJiP5BAAAAMBkJJ8AAAAAmIzkEwAAAACTkXwCAAAAYDKSTwAAAABMRvIJAAAAgMlIPgEAAAAwGcknAAAAACYj+QQAAADAZCSfAAAAAJiM5BMAAAAAk5F8AgAAAGAykk8AAAAATEbyCQAAAIDJSD4BAAAAMBnJJwAAAAAmI/kEAAAAwGQknwAAAACYjOQTAAAAAJORfAIAAABgMpJPAAAAAExG8gkAAACAyUg+AQAAADAZyScAAAAAJiP5BACwFem67t1d163uuu6s0bhdu677TNd1Z9ffu9TxXdd1x3Vdd07Xdd/quu4RW67mAMDWSvIJAGDr8t4kB84Z98okn+37/gFJPlv/TpKDkjyg/hyb5G2bqY4AwJ2I5BMAwFak7/vPJ7l8zujDk5xQh09IcsRo/Pv64ktJ7tF13d6bp6YAwJ2F5BMAwNZvz77vL0mS+nuPOv5eSS4cxV1UxwEAbDKSTwAAd17djHH9zMCuO7bruq92XffVNWvWTFwtAGBrIvkEALD1W9Vep6u/V9fxFyXZdxS3T5KVswro+/74vu/36/t+vyVLlkxaWQBg6yL5BACw9TslyTF1+JgkJ4/G/179r3ePTPLT9noeAMCmsnhLVwAAgE2n67oPJfmtJLt3XXdRkr9K8tokH+267rlJLkjylBp+epKDk5yT5Jokz97sFQYAtnqSTwAAW5G+758xz6Qnzojtk7xo2hoBAHd2XrsDAAAAYDKSTwAAAABMRvIJAAAAgMlIPgEAAAAwGcknAAAAACYj+QQAAADAZCSfAAAAAJiM5BMAAAAAk5F8AgAAAGAykk8AAAAATEbyCQAAAIDJSD4BAAAAMBnJJwAAAAAmI/kEAAAAwGQknwAAAACYjOQTAAAAAJORfAIAAABgMpJPAAAAAExG8gkAAACAyUg+AQAAADAZyScAAAAAJiP5BAAAAMBkJJ8AAAAAmIzkEwAAAACTkXwCAAAAYDKSTwAAAABMRvIJAAAAgMlIPgEAAAAwGcknAAAAACYj+QQAAADAZCSfAAAAAJiM5BMAAAAAk5F8AgAAAGAykk8AAAAATEbyCQAAAIDJSD4BAAAAMBnJJwAAAAAmI/kEAAAAwGQknwAAAACYjOQTAAAAAJNZvCVm2nXd+UmuTLIuydq+7/frum7XJB9Jct8k5yd5at/3V2yJ+gEAAACwaWzJnk+P7/v+V/u+36/+/cokn+37/gFJPlv/BgAAAODn2B3ptbvDk5xQh09IcsQWrAsAAAAAm8CWSj71ST7ddd3Xuq47to7bs+/7S5Kk/t5jC9UNAAAAgE1ki3znU5LH9H2/suu6PZJ8puu6793SD9Zk1bFJcu9733uq+gEAAACwCWyRnk9936+sv1cnWZFk/ySruq7bO0nq79XzfPb4vu/36/t+vyVLlmyuKgMAAABwG2z25FPXdTt1XXfXNpzkSUnOSnJKkmNq2DFJTt7cdQMAAABg09oSr93tmWRF13Vt/h/s+/6TXdd9JclHu657bpILkjxlC9QNAAAAgE1osyef+r4/N8nDZ4z/cZInbu76AAAAADCdLfXf7gAAAAC4E5B8AgAAAGAykk8AAAAATEbyCQAAAIDJSD4BAAAAMBnJJwAAAAAmI/kEAAAAwGQknwAAAACYjOQTAAAAAJORfAIAAABgMpJPAAAAAExG8gkAAACAyUg+AQAAADAZyScAAAAAJiP5BAAAAMBkJJ8AAAAAmIzkEwAAAACTkXwCAAAAYDKSTwAAAABMRvIJAAAAgMlIPgEAAAAwGcknAAAAACYj+QQAAADAZCSfAAAAAJiM5BMAAAAAk5F8AgAAAGAykk8AAAAATEbyCQAAAIDJSD4BAAAAMBnJJwAAAAAmI/kEAAAAwGQknwAAAACYjOQTAAAAAJORfAIAAABgMpJPAAAAAExG8gkAAACAyUg+AQAAADAZyScAAAAAJiP5BAAAAMBkJJ8AAAAAmIzkEwAAAACTkXwCAAAAYDKSTwAAAABMRvIJAAAAgMlIPgEAAAAwGcknAAAAACYj+QQAAADAZCSfAAAAAJiM5BMAAAAAk5F8AgAAAGAykk8AAAAATEbyCQAAAIDJSD4BAAAAMBnJJwAAAAAmI/kEAAAAwGQknwAAAACYjOQTAAAAAJORfAIAAABgMpJPAAAAAExG8gkAAACAyUg+AQAAADAZyScAAAAAJiP5BAAAAMBkJJ8AAAAAmIzkEwAAAACTkXwCAAAAYDKSTwAAAABMRvIJAAAAgMlIPgEAAAAwGcknAAAAACYj+QQAAADAZCSfAAAAAJiM5BMAAAAAk5F8AgAAAGAykk8AAAAATEbyCQAAAIDJSD4BAAAAMBnJJwAAAAAmI/kEAAAAwGQknwAAAACYzOItXQEAADaPruvOT3JlknVJ1vZ9v1/Xdbsm+UiS+yY5P8lT+76/YkvVEQDY+kg+AXdohy77lwWnn3r0726mmgBsNR7f9/1lo79fmeSzfd+/tuu6V9a//3zLVA0A2Bp57Q4A4M7t8CQn1OETkhyxBesCAGyFJJ8AAO48+iSf7rrua13XHVvH7dn3/SVJUn/vscVqBwBslbx2BwBw5/GYvu9Xdl23R5LPdF33vVv6wZqsOjZJ7n3ve09VPwBgK6TnEwDAnUTf9yvr79VJViTZP8mqruv2TpL6e/U8nz2+7/v9+r7fb8mSJZurygDAVkDyCQDgTqDrup26rrtrG07ypCRnJTklyTE17JgkJ2+ZGgIAWyuv3QEA3DnsmWRF13VJuQb8YN/3n+y67itJPtp13XOTXJDkKVuwjgDAVkjyCQDgTqDv+3OTPHzG+B8neeLmrxEAcGfhtTsAAAAAJiP5BAAAAMBkJJ8AAAAAmIzkEwAAAACTkXwCAAAAYDKSTwAAAABMRvIJAAAAgMlIPgEAAAAwGcknAAAAACYj+QQAAADAZCSfAAAAAJjM4i1dAWDrcsjyty84/bSlz99MNQEAAOCOQM8nAAAAACYj+QQAAADAZCSfAAAAAJiM5BMAAAAAk5F8AgAAAGAykk8AAAAATEbyCQAAAIDJSD4BAAAAMBnJJwAAAAAmI/kEAAAAwGQknwAAAACYjOQTAAAAAJORfAIAAABgMpJPAAAAAExG8gkAAACAyUg+AQAAADAZyScAAAAAJrN4S1cAmMbBK/5qwemnH/mazVQTAAAA7sz0fAIAAABgMpJPAAAAAExG8gkAAACAyUg+AQAAADAZXzgOt9GfLTtwwemvO/qTm6kmAAAAcMel5xMAAAAAk5F8AgAAAGAykk8AAAAATEbyCQAAAIDJSD4BAAAAMBnJJwAAAAAmI/kEAAAAwGQknwAAAACYzOItXQG2Lie+58AFpx/17E9upppwSx284u8XnH76kX++mWpy+xy67MMLTj/16KdvppoAAAAwpucTAAAAAJORfAIAAABgMpJPAAAAAEzGdz4BW8Shy9+14PRTlz53M9UEAACAKen5BAAAAMBkJJ8AAAAAmIzkEwAAAACTkXwCAAAAYDK+cJwt4oPvPWDB6c/8/U9tpprAz5+jl39jwenLlv7aZqoJAADAzdPzCQAAAIDJSD4BAAAAMBnJJwAAAAAm4zufgFvkkBPfuOD00476o81UEwAAAH6e6PkEAAAAwGQknwAAAACYjOQTAAAAAJORfAIAAABgMr5wHOb4248csOD0Vz/tU5PM96CTn7Hg9E8c/qESd9LLFo474g2brE5sOkcu/7cFp69Y+vhNPs+nLf/BgtM/svSBm3yeAAAAc+n5BAAAAMBkJJ8AAAAAmIzkEwAAAACT8Z1PADM8edkpC04/5egnb6aaTO+lKy5ccPpxR+67mWoCAABsjfR8AgAAAGAykk8AAAAATEbyCQAAAIDJSD4BAAAAMBlfOL6V+q/jD11w+qOPPfVWlXf6uw5ecPrBzz39VpUHW4vDl31qweknH33AZqoJAADAHZOeTwAAAABMRvIJAAAAgMlIPgEAAAAwGcknAAAAACbjC8e5Q3v3CU9acPpzjvn0ZqoJAAAAcFvo+QQAAADAZCSfAAAAAJiM5BMAAAAAk/GdTzCxZ684cMHp7znyk5upJgAAALD56fkEAAAAwGQknwAAAACYjOQTAAAAAJORfAIAAABgMr5w/A7i+285fMHpv/SikzdTTQBme+2KSxac/soj995MNQEAAH6e6PkEAAAAwGQknwAAAACYjOQTAAAAAJPxnU/cafzfDx6w4PSXP/NTm6kmAAAAcOeh5xMAAAAAk5F8AgAAAGAykk8AAAAATEbyCQAAAIDJSD4BAAAAMBnJJwAAAAAmI/kEAAAAwGQknwAAAACYzOItXQGAzemwZcsXnP7xo5duppqwqf3rB9YsOP0Jv7NkM9UEAAAY0/MJAAAAgMlIPgEAAAAwGcknAAAAACYj+QQAAADAZHzh+MQufNMxC07f9yUnbKaaAD8Pjlr+xXmnnbj0UZuxJgAAAJuGnk8AAMCd1kEnPysHnfysLV0NgK2a5BMAAAAAk5F8AgAAAGAykk8AAAAATMYXjt9Gl7z1VQtO3/uFfzfJfL/x9sMWnP5rz//4rSrvs+88ZMHpT3zeabeqPIDjT1y94PRjj9rjVpV36kcvW3D6oU/d/VaVBwAAbF56PgEAAAAwGcknAACAn0OHnPjWHHLiW+efvvwdOWT5OzZjjQBmk3wCAAAAYDK+82mO1W//xwWn7/H8P9lMNQHYun1k+cLf5fS0pb7LCQAAtgaSTwAAAJvBwSteu2H49CNfuQVrArB5ee0OAAAAgMlIPgEAAAAwGa/dAQAAW4Xnn3hgkuTtR31ywbiDTjlq9NeOE9aIzemIZZ9Nkpx09BO3cE2AuSSfALhT+dIJaxac/shjliRJznzH6gXjHv4He2yyOgEAwNZM8gkAAGAeB530xxuGP3HEP82MOfikVydJTj/ibzdLnRZyyPLjkySnLT12C9cEYOA7nwAAAACYjOQTAAAAAJPx2h0A3A5nv3nVgtMf8OI9kyQXv/6SBePu9ad7b7I6AbfdWW99cpLkIS885XaX9fl3HJIkedwfnHa7y5plxXsO3DB85LMX/oLt2+qd7zsgSfK83/vUJOXDndVLV1y4Yfi4I/fdgjWBzUPPJwAAAAAmI/kEAAAAwGS8dgcAALfDF48/dMPwo449dZJ5fPzdByVJDnvOJ27T5z/43vL63DN//+fj9bm/+mip72ueOtT3lcuG1wxfe/Q0rxluKYec+I9JktOO+pMtXJNb5tBlH0qSnHr0M24mbtmG4a72e/j40UfNjH3ysuFV11OOfvLtreJtsnT5GUmS5Uv/x236/FOXf3/D8EeX/tImqRNsLfR8AgAAAGAyP/c9n9a87f0LTl/ygmeVuLe/Y+G45//BJqsTAEzt0v/7nQWn7/XyBydJVv3T1xaM2/OPf32T1QkAAGb5uU8+AQCw9Vn1tr9Lkuz5glfd7rLOP+6IJMl9X3pSfvimw5Mk93/JyRumf/ctZdyvvGgYd+bbhtd+Hv6C2/+f726L095VXrU75Lm37VW7JPlw+4943cJx7z3hSRuG19ffzznm07d5vm/8QHltbl2d78ufOd3rfi9dXpbxuKW3/1W8g05+XpLkE4e/83aXdVsdcuIbkiSnHfWyyed16PL3JklOXfr7t+3zyz5cPn/00zdRjRZ2+LK2L5QXeE4++oCZcUcs/9cNwyctfcIkdXnK8m8nST629KG3u6w/W3FxkuR1R97rdpfFxsb/bdh/Ft6yvHYHAAAAwGT0fAIAYJNa8/ah18iS5z9vNP6tddwLb3FZl77trzcM7/WCYXjlW16RJLnni/7hNtby9jvjn+sXjY96FX2hfvn4Y+Z88fjn3nFIkmR9jX3C807baPqn3nVwkuSA555+m+qyvPZwWvrs6b6I+/h/Kb1M1o/GteV54bNuXc+mv/9wKevPnz587m8+Usb95dNufy+pZ68o6+M9R34yTz+pDH/4iE/msJNbT7Adb/c8Dj7plUmS04947TBuxV+VcUe+5mY/f8iK1yVJTjvyz253XQ5Z/vZS1tLn32zsocvfnSQ5delzZk9f9r7RX6WvwqlHPyuHLvtAHf6deT73kTr9abeozs1hy0qPw260Iz15Wdl/Tjn60Jmf2RSOWv6fSZITl/7mzcYevfzrSZJlSx9xm+b19BPPT5J8+Kj75nknXpAk2bGb3R3xL1esTJL8zZH3zN+uKL12Xn3k0GPnDSsuTZK87Mi98uYVq5IkLz5yz5llnXDimiTJMUctuU31Pv0jlyVJDn7a7rfp87fWN9+5Oknyq8/bY8O477y9LOODnz97Gbdmq970b0mSPV/y+A3jVr/pM0mSPV7y21ukTreH5BMA3IFc+g/nLjh9r1f8wiTzXfXGLyw4fc8/ekySZPWbPrtg3B4veWKJe/PCN9B7vLjcaK9+y4nzx7xo9n9EAgDg58sd7rW7rusO7Lru+13XndN13Su3dH0AALZ2rr8AgCndoXo+dV23KMlbkvx2kouSfKXrulP6vv/vLVszAICt0225/lq75vKsedv7s+QFz8qat5VXdJa84Pey5m3vqYUOsWvefnyZ/vxjNypj9duPa6UlSfZ4/suz6m3lNaQ9XzD7NaRL3lq+fHzvF/7dRuMvfstLkiR9f92GcX1/dZJk35d8cGZZP3hz+ZLxB7745JnTx77+9sOSJI94/sfz1X8uw/v94cdv9nM35/+9s/QA/F/PG3oKfqK+freQk99dvoh8vi8R/1h9Be8p87yC9/73ltfbnvX7n8r76vDNfSH5LfGm+iXjL/md2a/N/cOHyvRXPGOY/n8+PPrC6Hnq8OqP3fRL0/9kWRn3j0dP95rhQSe310O32zDu4JPK656nH3HrXvc8eEXbZhfd4s8ccuIbR3+VPgOnHfWSHHLim+vwi295WcvftWG4m2dFH7r8hA0Rt9ehyz62YFmHLVuRJPn40UfebFlPXlb2j1OOnr1vHLGsvIZ00tGzX0M6cvnnkiQrlv5Wjlz++Tr8uA3Tj1r+xQ3D3ahvxtLlX0mSLF/6GxvGHb38m0mSZUt/daN5PHV5O1wO7fu0E3+YJPnIUfefWa9b43X19bs/O3L2F2b/84mrNwy3G/znHrXHRjEfWL6mTu9uErt06e456WPlFbsjnrJ7Pv7RMjzeWj/54TLuwKfvns98qJS1qB+mb1Pfx/2tZy3Jf/xLmf7Y312SL7yvxo7e113Ulw/+xrP3yNfeVer+688d6vut48u4hx278TJ8922rblrWulLW/V+6V87/p/JK4n3/eK9c+H/L8Li3zSWvK6807v1n98wlr7uwDu+bS19/fgno1iVJ9nrF/XPpP/6gDP/JA3PpP35vo+lFqcReL39oVr3hzCTJni97eFb909c3mj4e3vOP98+qN36pDP/RI7Pqjf9Vy63TX/qbWXXcv9fh/7nh0/P1Ol/95nIs3ePFB2T1m9uX8Y/mW8vd40WHZfVbTq7Dh2f1W1ZstDx7vPDorH7rR+vwU7PmrR+aOb/b647W82n/JOf0fX9u3/c3JPlwksO3cJ0AALZmrr8AgEnd0ZJP90py4ejvi+o4AACm4foLAJhU1/f9zUdtJl3XPSXJAX3fP6/+/btJ9u/7/iWjmGOTtH7bv5Tk+3OK2T3JZbdgduK23rg7ct3EiZsi7o5cN3Hibk3crJj79H1/2/5ND7fILbn+quPnXoP9OEN77T5jeNa4zRFrvuZrvuZrvuZrvre/rJ026TVY3/d3mJ8kj0ryqdHff5HkL25lGV8Vd+eOuyPXTZy4KeLuyHUTJ+7WxN3Ssvxs2p/bev01bq9Zwzc3fapY8zVf8zVf8zVf8910891UP3e01+6+kuQBXdfdr+u67ZI8PckpW7hOAABbM9dfAMCk7lD/7a7v+7Vd1704yadSvlz/3X3ff2cLVwsAYKvl+gsAmNodKvmUJH3fn57k9JsNnN/x4u70cXfkuokTN0XcHblu4sTdmrhbWhab2G28/jr+ZoZvbvpUseZrvuZrvuZrvua76WI3iTvUF44DAAAAsHW5o33nEwAAAABbk039Deab4yfl3/t+c/TzsyR/PCemS3JckjVJrktyTpIPJdmhTn93ktVJvpPkI3X6V5P8R5Kzk3wmyS6juLNGZb85yfVJ+iR/Nae8cdzHatz1Sc5Kco954j5Y63h9kh8kuec8ccfUuq2p8959nrgVSdbW8n6U5OB54p6V5IZa1tokn61xa5JcNVoPXxjV74q6zt9dP3dt/fuUJC+sf6+r5Z6d5Lxa3ll1nk+t9Vhf476R5FeS/FuS7ye5Mcnna+zRNaav8/52kj3qtHvXz3wjyYVJLq7z++vaplfWOl+Q5I+S7FCnX1fLOz7JvrWMn9TxFydZVtff9XWdXJfkezXmh7Xsf6zLfHldjhtruz06ZVu8rv58ctRuP63l9Uk+neTzSb6bZGX9+WbKv6xu7baqrrur6jo9P8kf1fIOrNNvqL/fVpfju7V+Le7f57Tbd+aJa+12fZKLkhw6T9yrR3HfSLIkyZeTnFnjXjNa3mtr/c5Mctd54k7OsP09pbbRrLj/qnHX1/W28zxx/55hf/tq/dkopsb9RW3P9XVZZ5X1tdqu19V233+BZb281m9lkpfPE/f9Ud2uSNlfZsX9fTY+Fjxonrh3jOI+k2RxHb+ots2p9e8/qXE3JDkjyXbzxG3UFqN1NTdubltsO0/cuC2+kmTneeL+IuXYe0WSaxeY79z2+NUZMV1drzfUn/fPU9bctjhpnri5bfGL88TN1xbnpxyzvpmyLR5T635NkksznGM2ipt1jpkn5vUpx77rUo4Tfz1P3IF1mS/LcN6YFXdMyrH5hpTjwOtSzlnLUo6B3035j2hHJ7k6wzF+l3niTh6t52+nnNf+KOVc+J3Uc3Zuely52zzn/bYc5yR55Za+DtnafzK6ZpgxfGXdlq7JsF9eXNvwhpTz9voas370087n/ZyYtTPGz41tv9vwjfVn7nza59eOyv7mKOayDOft8Tz6Ufy6OfOd72cce9Wc8W1+/Yzh60Z/t/qun1P2ujnTr51R7roZ625uHWeVPbc+7eeGGWXNiptb77nLOW6H8Xoa13XtqJ3WZf56zdoW1s2I61O2v2tu4bLekvYd/1x7M+PW3oqy5vuZr55Tlduu1aeY55TLc0ery3z72JZYF+s38/x+ntpp6vnPdzxZt8C0W1OPm6vbjbeh3Gsy3BffMBp3YYZrxutrzJUpeZRvZ8ipfHsUe05K3qW72euLLX2BswkukBalXMjfZ874g5P8a8oN/P9MufH6aJLfr9Mfl+QRKTeMb6/jTklyZh1+ZcrNR4tryZNd64reP8l/plyk7zIjblEt+wFJtku5cHvnPHHnJvmFGndxSjJs1nzPTfKQlCTRjUnuP0/cFUn+stbr3AXqd0nKRdnOdQO6IMnvJjkhyaU17i9qea1+q5O8qZZ3zZzyrkny7CS71fl+uS7LcSkXrQ9I8q26ER9a/16T5K21bm+sbXRFXc4rap0eWev6zlH7Hp/kBaP2uKAu53lJHlzL27uW327ifz0lcfnvtT6H1Li7pSRIflCX/We1brvV5f1Akj9O8skad3aSh9dl/B815uKUm63H1PrtknJh8rwa98hRu30wyXE17p51vo/KkPDbJWW7ejXQ/HIAACAASURBVGyt30NStqUf1OGLUxKCO6ckJQ5P8oha3l1HcT+c027vmieutdt2ta7fmBH3qFG77VLX6+szJBW2TdnHHl2X46W1vMuSvGZG3NNTbiQfWJf1vJRtaG7ck2rb71rne1VKQmVW3HmjuJ8k+T9zYh6Zsh38oLbp1XUd3W1G3S6vv+9XY+ar2+okH86wfz5gnrhz5yzD8+eJu6HWc5e67j44I+6ZNe7BtX6XJ3lejXl5/cypdX5XJXluLe9nSf5kRtyDZrXFPOXdpC3miZvbFq+ZZ75npmxbJ6acFGfN90HztMeGmPqZF6ecHHfLkNTZZUbdbtIW8yzDTdpiRtyDF2iL85PsPuf4fVzK/nBu/f3347gFzjEXjGNq3IEZ9vHXZzgOjctaVGMek5L0viFlH501z5VJPpdkzwzb8gmj5dkuw/nldfXvS1LOa3Pj7puNt4HLahuflWTHlO+b/H9J9suM48o85/nx8ezMJA/a0tcfW/NPRtcMc4aXpiSh16WeU+r2eUaS7ZN8MeVG/NC6XfQpDzh+Vsd/KOXYuy7Jf6ccj9emXCtcUrfRa1IueC+o23+7eP90ynFiVd0G1tftbF3djs6vw5fV+V2Z4WHX+pTrnPbQ68oMSYwLah2urH9/OUNi7cokn6jjf1Lrc/7o839dl6fN4/UZEtvXpCRaL61ltePr2pRjz4U19rL6+4d1el/X48vr586qdW43CD+u62B1nf6olGNVS6hdVZf/2lqnZbUu61MS26sz3FD8rC7P1bX8K+vnr6/TzqjDl9f26evnWuLvwlqn9vfKOt8b6+8z6/KurXUYP/D8m/r7xpQHpV/Kxgm2tTV+/WieLf5fMySmLql17eeM6+v6bG3zuTpuZV2n7YbtqgwJvvWjstYm+XgdXlPbsQ33tQ2+PKpjq+f5Gba7b6Tcj6xLfchSh9v6vjbDw9If12nXp1ybX5lhu7wu5SHmjaO2awnYn47abuWo7u1mdFWdd2u7tm4vznAT2h52fCtDUqqth+9nSBaeXefRErjra9u1/eEnKQ8vWyKw1bdtmxfWz9xQY8+tw/9R57emLk/bz9tyrK4/7Vjw07ru2vKsrMvY2rHVrdXr+tG6b9toa/+2fbZ117aZPmWfXF2Hf1rbu20na1L26bbu27b97VGZ59U6tu2/JVuvS9ku2sOy9kDn+tHv9SkP+dq6vyDDvnLpaB4/quNb4v/SDNv010bza8v049Fn31zX2421fnMfLHxjVH47brXjwdqU/ait63fXsm+s66atj2/W6WfWebU2bMek9gCj1alP2TZ+Uud1Wo2/MeV4elWGhMmVKeejtt+fWaePjxVfz3DsPKeOvyzDsfi6lO2vJcjbtnFhbf+fpjxUG6+3Vu+2D7fk0nm13u2YuK5+rj2caPvL2pRj1I9G7dOnbAcXjGLHD0raOmrbwdoM++jrR8t43mj46vrzo1F7rMvQcWe8/ttx7ISUfeHKumw/TvIvSfaoy/aylIe9bfjLKeerg1LOlQfd3PXF1vDa3ROT/LDv+x/NGX94ygl3ccqB4B4pF7crk6Tv+8+n7Dx3TVnRSUlM7NN1XVfHHTGKaw5Icnrf919Oafgzkhw4I27/JN/u+/7svu9vSPkPMvvNE3d23/fn1rhvpyTSZs33MykXOi9P2VCeOE/cD1N6EVxRPzNf/bokr+j7/qqUm6mrUg5av5myUaXWZ7u+789N2UC3S3J9La+fU941Sa7p+/7Hdb1sV+d5TY35g5SLi22SnNb3/dl1vTy51mXPlC87vSLJE+rnL6rr+T0pbdr0KUmjA1J6FVxUl/fTSR7W9/3XU3rSrE95Gn+vvu+/1vf99+u8Fie5rO/7r/d9/7O+769MObjcPyXxdnZdjk+l3FiuStLXuP9OSWieneGkdFaSPfu+/0KtX3vi/5jaBq8Ytds9U0766ft+ZZ3vYbWsdXU5TkuyT12O76Rc1H+/rpdFKT3urkpJfDyoxmW0HE9Ics6cdrtinrjWbq13TDcj7rBRu11R18szah2SkhjZNqUX23ZJ3lrL+9g8cY9L8t6+79uNx3lJ9p8Rt3+SU/q+v7zO91tJfnOeuE/3fX95yrZ7ccoN8DimT3JEXX+vqH+fU9t3bt3OS7K27/vzasx8dUuSV9f5fiZlH58V95kas7aOu2GeuGvKau+vSNmPd5wR95tJruz7/ju1fmcneU7XdfukJFTfWeMPqMt6Qi3vs0l+b0bc4bPaYp7ybtIW88TNbYsHzDPfjyT5P0lelLKvzprv4TPa45A5MUlJEJ/a9/2PR9voM2fU7SZtMc8y3KQtZsQ9Y1Zb5Kba8fuAJG+vw5embI+zYueeY+4yI+6nGfbx/0o5t+04J2b/ur5eluRPUy5CDppnntcm+du+71fV+j02ZV94V8qKuCHlHLldkjfUv99Tl2Fu3KNSt4HR+rt/ki/1fX9N3/drUx4CvCwzjisz6rd/W9Za/oez8fmATWx8zTDn+uEZKUmDpFzQXp7k7kle1ff99Snb4BUZkinrkuyTIdGyJOWc9LOUZGK7GfpiSuL47JTz3eUpD1mWpNwobJOSEF2fcly7Z8p5ea9axpfq8A9SttG1Kef+XTLcdN0t5Ry8Vy3jBynXA/+RcuG9OGUfuXcdbjfGv5FyfbRtynG0XUdcnbJPbJ9ys5WU65gfpVxb/keS96c8KDo35eJ915R9f6eUc+uFddzaDBf461POS1+sy3jfurxrar3OrPPeKeXG6rdSkt8ra70urOvr2gw3YtvUZd+3ts95GXpZf6cu27X191V1XotSksQtwff/6jJeW6d3te0uqsNJOedvW+uQlJ6WP6nzb+fcpNx4PasOX1jX0S4px7XUuq3L0CsyGW70VqYcE9pN2Y/qMiVDInBN/b1zBi3B8omUhy0ZfWZdhn/CdHb9/dOUbWZ9yro+qY4/rv7eebRsqXHbpTxEbeftlmRNLWNRhpu8Nu9rav13TlmP29Tx26XsJzuM5nFdLWNxXf6uTk/K8XP3OrzNaB67pmyHSWmbrpa795x1kJTtrKt1bHZPuRFNSnufXWPazw9S1us2Kdv5ujqfazJsF9fW4d1T2qHV/8w6fXGGBxN3qeXtlrIeF9fPf3e0Htq6TC1327puUsd3tfzFo3puU3/vnI3bbNFofhn93dbdd+vwDinbQ1/L2THl2JdaXivzzbWMbZLcK2U/bg/8W32vTTlGrK3TzsqQbFxbP3tjyoPu8+rndqnrdvv60+a7YlTv9bVOa+t8WvK0HRPX18+2up6Tsr5b8mZRhvZbnbI93D3D/rtjnb5NhoRLW7+fr7FX1rh23X2fUf23r7HX1Tq1ZehGZY3XdZI8tI5flNLul2bYV29IeQDSPvuFUWxS9uH7ZDgH3KWu+51Trq3aMj80wza8bf3skpRtYps637tmSNa0ZV5f69L2tb1T9q0L6/ir6mdbmcmwX+yYkptIhvbcrs63LU/bPq4ZjWvTF2VIuD4/Zd/cNmX9t21/+7ouW9Iv9bPt/jg1fruUY1mXsn/ukXJcbcnQ/ev8rk7yyynn0KszdOA4LuVa8H2ZfV27ka0h+fT0lCdpc90r5eboH1KyhPdLuXn59Jy48Ulyz5SVv1vf95ekrPxZ5V44+nt1HXdzcQ+usTPjuq77u67rLqxxZ80Tt1OSi/u+b0+S9pon7mdJXtx13bdSniz/4jxx2yZ5bNd1Z6T0jrhPys64pJaflA207eSPTbkgbCe67ZPcv+u6L6VsbJ9L8vq6HEelXJT9cpL31vgHplw89km+13XdgSk79u4pr7L9aR1ekrKjLE7Z6N+T5HeS3KMmBpOShHtWyitnByZ5SR1/UZJHdV33nZTE2V+mvKJzRtd1i7qu+2ZKQuiMvu/PaCuj67qPpTyp3TXl4NX8Vq3365K8tOu6+yb5tZSD6P4pF6GLUnpnnDWax5qU/euc1HZLuTneKeXC9k11vvet6/UPUw7yO89ot6UpJ7+H1/Uybrffq/XJqLxfq3Ft+5vbbnPjPpeh3Q7IcDEwjrs65YD/5DpptyS7j5Z3dcoN6/qUxGfbfr6XZLd54sb7x8ok95oRd12L67pu25SL8JXzxXVd955az7vU9tgQU9v7wCRfrPt3UraXfWfU7eokf1fb4l5J7jPPPHdO8rSu676a5PFJHr7QMiQ5MmWb2HWeuI8lOb3ruotSjllfmWfd9V3X7VfL3LnW8Z+S/FmGE8oDUpKKrS3OSTmmzY2be6xaOU95G+LGbTFf3Jy2+PY8cb+SktBqT6nnm+/c9vj/5sQk5aS/e9d1X+267hMpJ+s/XGBZN7TFPPO8SVvMiLtHZrdF6vJ8uuu6r6UcDy9MSVBfkrLd7ZTSHhviuq47NrPPMdvMiZm7LM/JcIE7t6wuG5839p5nnjtmOK48KeXBzpok7+m67htd170zJVmweLT/fDflgmxu3P2y8XntvikXpo/rum63rut2THnqe5/MOK7kpuauk4sy+7zLtO6aci5rN2PPS7mR3ynJb9dtZ/eUdnxPSnJxfco56xdTjnGPTblBuVvKNcTParn/UON+KWV73yXl+q494V2fcs2zTco++9GU7W77lH39F1IulFen7IdrMrye25IJ+9Tfe9Yy2zb09JTj1PY1fs/6e+9a193q9O2S/HZKL+oda72fkiHJ0aVcq9y/fv7RKa/N7phy3m83vu2mf/8MN/s7JDm2roNt6mc+XafvWMfdrcY+JmWf2j7luuA1SV5V26ElmHaqP9+pMS2h8NCUZMj2KddYa1P295ZEWFzruGdd3kfXee1cl/fGlONeO448uK7XdkP2nJSbu3vXedyzrr++/t2ua/ap5V6Xcry4R8o56+6j2G3rMl85qu/1tcwdUs4Li2t73DtDj41FKeeWbeq8243i/6q/H57hZnBRyra2OEOi4pfqtLum9A5v7fPsOo92/LuhzicZkhhdyrXekjr8kPp3u6ltiaOW3LhHhp427WZ8cZKH1fhdRp/ZOUPyqY1rv5NyTG3D7Vq5lXdMHW4Jg51qTLshbUm6tr4W1+VPyv7Wynt4yn7V1l1SHj7sPJr+mBq/W43ZNmUbbuuxJSB2T9mftk25ie1SkleL6/palNJzJynbQbuWaYmetp2klrV7hkRUW0fbj8a19XLXzL7/bet/n2ycsG6J921Tzj3XjeLbcrckQVLeIGkWp9wHZVSnLqXdX5YhkfMfGR4wblc/t13KvrRvhmPH9vWz9xjN76DROmvb8E51WkvCdinX+21aS9C8pg63Zdkuwza1d51fW19tm71L/Wmfa9vQpfV3S9DdUGNagq7tt237auuitWdbhmTjdtsnw/a3JMPxNSltse3oc4dn2NYWpWy7bfvdJuUcsnP9zFNH6/ruGRJvrezt6/jtU67VFtfPbT9aFy1x1uq3OEMP/NR1sG2GxH2XcpxrDwNa79uMPr9DhuRvSxavHa2THTJsz/vUsnbO0OZ7zKnXNinHk7b/LEo5xrXkXmu/VXW5fqF+5qqUdr5byjo/OuXYd6/6c0nKMfyiDNdkt+ja7Oc6+dR13XYpF60fmzU5ZYc5POXE9p9Jdui67lkzYufqF5jWzRg3K35DXNd1r0rZuL4/X1zf96/q+37flBuIh86I2y6lt83/XqBurbwzUjaUX03ZeA6bJ65LObE9McMT8ytnxDXPSHm62Jb30SlPNp+ZclJ+WJKX1eU4PuVp5XdTkjpJ2amWJPnnlAvEUzIcJFqPpxenHITbwf13+r5/aJK/Tdle20H9GSlJrb9LybT+S9d1bXv+Ud/3D05ZX29I8qe1d9O6vu9/NWUdP7jruockSdd1O6fsbE9LucH5hTr+VSk747uS/HlKwmt5yoXEf9fh9vrdGUke2vf9upSeKWenZI2X1Hr8777vn52ShV+TkrDYuZbxvCSvTXlq9a8ZtVvXdQ9OSXzdvc73ugzt9siUHgAHdEUrr8XNbLcZcb8yarcPpPSGmht3Q8prMy+qN9PbpfTSaut0n5QL6X1yU/2MuN1uYdw4wfrWlO7s584XV9dxe0L8sHFM13WPS7nwnZuAnlW3j6ZcMPxGygH/iHnmuSjJdX3f75fSA++pN7MMz0hJis+3rI9OcnDf9/ukPNE+bJ519+Ykb+i67sspF/3bJ1nd9/3XRvOadXyfFTfrmPaIm4lrbTFveXPa4qAZcTvV5XnTLZjvuD12SXlAMI5J6o1UbYt3pByXrlxgGVpb/PI885zbFr87T9zctmgn8sf0ff+IuuyPyfD0r+lnxL0o9fgzx5vHMXVb7pINx6m1Kb2fls0p60Epx5PxeaOfZ57thv+RKcei9lry2/q+bwnoI2fULTPinpBsdF47M+Vi5+9TEqifzPDa1E2OKzPKv6XnXaazQ8r5rG1L61Iekhxc/z405Vpil5QEwS+nnDfWpiRVfpRy3rwyZX/6Xv273ch+LmV7aDcwK2vcjRl6N70/w+sl+6Vc47Wn1e1C/iG1nDeNxrWnvD/NkIhanPJKydqU8/6nM/QQaK+R/CAl4dySGjek9EpuPbK/luEVkAtTEmkrM/R0WZvymvj1KTcL7aFUu8jfKUOyoE/pFdluKrZJOT5dnqEXR3tgdkEts/WmWJFybts1Zf9rcV+t7XC/DDce90p5wLlvLWNZndf3Mty0/HWNvTHl+qBLudY7MMPr32dmSAy25UvKzewOKQ9yr67r+IJa7lkpDwSTcj28PuXm5lsZXgUcv8LSntaPewLcNWVbWpSh50hLFKxPuSlfn6GXU7uxa689JRv3emo35etGca331U9SrmFbb4C3ZOOeRtuk3KCvGn12Xcr1bEtKXJRybdle22o3xS3Rkbo87Qb9/TX2A6P5jntafCHDa3Vt2dq5vr1m1Dyt/m6vviXDjXX7mo+WVPhyhtfykvJaTrsBvj5Db6Q1GXqZtf2i9SBMNu7t1Npl3BOlvfaT+nunDMmRbTLcg7Rebo/KcKxv59D2+lDrbdj27X70d9sGxjf2F42mtdfd2nb71VFcn6E3S5fyELotxyMztNV4fmOt7VubtQcne2XYDy9Nub7euX7+ESn3Q9/OcK94aUoS+AcZvjf2mgxt1ra3u2doq/Upb7K03luvqsu4PuXYfGXK/XBr6x1TtoX26uKiDNvWd0bL+tMMr+5dUz9zWoak6fqUB+Ut6fHfGXr5dRleT2zrrb1a2fbRcRKmbSPj+NaT61sZ2jEpx6xVo9g1GV4nW5vSG6h1/Fifcn/Zvqe1vbbWeps1rayL6+dboqy9pnp1rUv7vuBxj60bU5JL7bh+jzrt5FEd3lGHl6Rs/623Zlvv4/3jW6Ny2va8JsOri1eP4n9cf/9Xhp6MrU6rMuxb43WblG1w/PeDsvE++cNa3qszbFdtecef62eMm+nnOvmUcuH89fqawFwXpfTiOK/v+zUpJ9wTU24oxm5MOQknpXHukeTyruv2zuyeSheN4pOy4a+cL67rumNSLspOWShu9Pd5uelNSlI2yt2TnNl13fkpB4SXdl03t/fTRUl2rTes6+vfe+em2s57csoB8BspO9HuGbp2J/VLLruuW5zy9P5Ho+VYnST1tY+vpNzAnFinrUzZsT6S4TWPi1J2/O37vn9sHW5d+l5c57t7SvLmsGx8cr1fysbfXnV6bsoN6UX17x3qZ/dJ6RmzbcrrPCtTdpy56/JrSQ6sccuTfKDv+2Up3ap/ZUa7LUvJkn+g7/sT63z37fv+uxneqb3PqLz31c/8Ym7abr+c8nR0+ZzyLk1y+Kjd9km5qFyT5D2juPVJTuz7vj1ZaL3g5pa379x2m7O8Le6Bc9ttnri79H3/pL7vf73WqT39S9/3P0m5edgjyV3qfFOX9fIZcXfPxtv9PTPaP0Zxe9Xl+KuU7enbC8XVcetS9uUHzol5fMrF6htrW+yYkoBYOSfu7knu2hftuzs2JNXmzPOaup6ScmDfa564fbuu2y1l+716nmXYO+VVy3bj8ON55nv3lFdfH9v3/f4Z3ll/cl2uD6fc/P+vlFfFWlv8Ysrxbm7co3PTtrjXjLgnz2iLJfPFzWiLuXFPr/M6p45fnHIcmFW/cXt0Se49jum67v0pye62ra1ISdL98jzLMG6LWcvwzBltcc956ja3Lc6ry962q9UpFwMPSbKqnlv2Sdl2Vs+JW5HylGnuOeZ7c2L2T9kn/0fKcep3aplnz4nbJ2V7acefXTP01Jg7zxsyHFfaze7K0TpYlrKNrq3LkJQb0ytTXnsex+02ZxnWJPmVvu/f1ff9I/q+f1xtq3OywHFlZO55cp/MPp8ynftk+L6t/0y5oflqhtdodkw5FrYey6eknKPvUqe/IOUG64yUB0f3S/netPZdKafVuF1Tjme7pCQs75KhF8Tv1jpsn5KIvk+GVyIeUD+za409LuWienHK09nFdXrrBXSXlF5YizNsW2dkSEasqeN/mHIdtH3Kvv34Wt4XU3rbtFdBHpDh1bG9U/bv9tT4hxl62LTXMNr3h7TvDWm9OFqS4KqU1/DajcFeKTe+SbnWat/Nco+UREFLUNytzmdtSqLibin71GW1PqtSegol5Wn8E+rwvWu9u5Qb1htTrkl2qMt/VoaEyekp+2DrpXJlhhufR9ZluFvKTfSpGXqSPCxD8ukBGb7n5j4px7i2Ltv3pXw15VptbW2TG+q8zsnwHSrtO2jazeP4NauWHEnKTeZ/1eFxz4L2ufY6zaLRsrTeH+szvAaVDK/q37Wum90zvGbV2rndELZXoZIhsXZ9hhvBlSntsVMt/yH1d0twrc6QILkh5XXllgBoryaOkx/jpNay+nvblP0iGZIDydAztvWm60aff8Rovu1msks5X7YeUe1V1U+PYtr33SRl3bab45ZoGCeEfpKNv9g+Keu23dy3ZV+f0ib7ZkgqbZOy3lvPoZ+NlqurdWjHjrYMLbm6LkPSpX1mnwxtcvFoGS7LkABox7lP1fm1VxtbUjSjz7TlWZ9y7ZBs3Evt6gyJ+7Up1xJX1zo8MENSqU95YN9ed/tJhtefWplfz/CKW5L8foZ2befq1rNo55SEXupyLcqw/+yR4ZW/cSKyJX1aojCjzy+pw9ek9K5r+9xv1HLa9tSujVvbjHskJsNDhIziUpe3vWK2NqVjxXUp+1KL22sU/5AMx9NFKdtm6x13bY1dl+G7BHcYLWdLOray7pmhl+LilGPtTzP0MNotw9tB4x5b245iWnn/XP/eJuUraFrdt8vwWuO4x1/rhfuglGNc+5qIH6e0YUvm3nU03LazIzL0amoJ3XFb/ShD8qol07fJ0MuzPVh/WB3f6vCDlGPGygy5hR+l7DvtmuyWXZv1d4AvtrytPyk3AM+eZ9ohKSea76T0PPlyyvc4vWQUc99s/IXjH0/yrTr8yiT/f3vnHuxXVd3xz5dE8VFrQkHHN/hiqlJpUbRWKkWtDrWo1QoWpwrWKWp9lmqnzChVqYaiIoyKFBQpEi2WwYBSNAIGVCSIhAQEkpurISYBkxAEzOMmWf1jr83ZOfx+lxu81yT4/cz85nd+56yz99qPs/f+rbPOOic2cm1A71HK5HIlZZDaY4DcdMqEvzg7xALg2UPk6mOB9U7Et+8n35mUyenpQ+SWNXJrKH8oBuX7S8pdnVPyeyXdRVIDjv8b5eJ/M+WxiQWUyWEmZYBcRJkAFlMGlCMof0hGsu5PonhBLaLcNZtN6axPy7LeRXlk7mzKHca3UDwJpme+p9AFth2lCw58ccruQbkIVqZOt1IusLMpHksr6B7lm5HnzqMY216Vcl/K/aI8/lcDf/9ZU95LgdtSbh/KwLCMYihbkZ/LsnwnUya9K7I+RikTeW23M7JuTgae0bTb7anXTMofs2psOLnps7Xdar3Udjt7gNzSAe02SK6220ObduvL7ZHtVhf3d1GC0dc6reU9LOvvXXQBxz85QO4dqdPudAFcHztA7ogs748oA90oZdHal3tjtn3t978CTu/JvCrroOZ7T+a7xwDdapytfSiTzSlDdFtHMZjMzHa4fojcKCXm1+xxynAEnTdBDb78f0Pqruq3L2VCfVnTVgfTBc2+h20Djh87QK6tk9oW04ak12+LPYbItW1xJ8VzZ7x89yEfWxgi12+PWa1Mbp+c+s3Mtt7YtG2rW78tBpVhrwFtMed+dLu3Lcj4ACn7SMr8s4ou4Phofn+6J/cDimtzO8esAJ7ck3klXbDSAyjX2kLg+T25QynjQJ1fNlG8UwbluZriZVnH0V9Q+tu+KXs8ZTxfR/FgquPymQPkTmXbPrCGcqOjfVvpTalXf1y5d44eMJ7Vctw7n/ozpWusvenWDP3tMcrcujflmryDcg2OUq7lz2c7BcVgM4eygH4b8BHK9XUOnRFmEV0Q5Rp0uM7tQRdYe0vuuzi3F9Pdwa6GrGvp3iZb/2Ssp3vpxwjb3v3eQBfcfGv2/dV57FS6R1eqx9MqOq+pWXTBd2+nGExqsOYxigfAiizDCF3Q4aUpX++6b0qdq1FplHKHfEvWxc/p/iTeTRnbq+fRG+gC9S7Jsq6neM8EZQ3wqdye2+j2lkynvhgkMq9v5PF5jewn6f7UHUUXmHc52wYcr0Gzq0fZeXQBrOube6PXdpuz/msg52pU+UVT921draUYNqsXS23nqsftWV/1j3A1Us2h60vV+6MG/K7eMK0XzUaKoS0ofW5Zbte011DGv6tS3xrk/WY6g8qvKX1ijC4Afvso6Qo6w9c9lDmnGvO20gUZDkq/Oy/1vJMutksNXr68aYfNlJAJkfVYPTSqZ8Umyjqt1nWNDXQ5nXdHDax+Y1PX65tzlmX5Rpp01zY6j+XvamCtbVrLW71haozUqvcKuuDRNc+b6N4qW6+3u9n2pQHVI6q+bbn1qKkx6LZSrpEanHsD3Ru/bsrzlzbtMyfTrmmto/SFpVlHVZfVzTmXNPW9gq4P1xtk67Mtk3JOkgAADFNJREFUv07n1bOabQP9V/3X0wWqr/W9lWJErnX2q+ac9ZTrqgaOr0ba0dR7C2XO35BlGaP0tRrEvtZJG9S76lv7WvU0Wk3XN2qdj1L6+PJM/y46I2Mt08ZGvnpvLcs8aoD4i+i8vdrA2CP5u17X6yjrj3qN3ELXx+o1WWPzjlHGyho3rh3/q5G0ej5GpruSznC6gG0NvrXf3E4X+Psqumuw1stIfiLr7uAmjVWpx6r8vbKp9xoPrvbLOhfMo3vRQK3HmmdQ2r/mvyX1W0Jn6F2U5ahB6ddlPtUb7ezU46d57mrKeHtryvQDjs+nW29eTHliYPy1xY5e3PwGi6JHUC6YRzf7jqEzTojiIlsHwiWUu0i7UxY+87Jyx7LSb6MsWL6fDfddymJ4Np0Veznlj9ypdB39TspAMzvTaOVW0Lm7rqS4VA+Su5Ju8P0pxbI4KN+jsxxL8tieQ+R+0KT3E8ofxUH5fpBuMhrLdC9n2wv1RsrFWheX9bXc36GbUMYo8R2OpXPL3JTp/YxuEl1OsULXDr6ZcrfzxXQTXA1keSglnlHrInsaxRr7PMqg/X3KYLCMMtgsoRi6RukGzFHKXcF30AUP3Uq56H5Etzipi9PL6PrFlvyulvc66P08P/XCHct2ey/dRLo50zk063p9s/8yugGiLhJHsl420fWXOgDVCWFRpndY6rGR0sfe3dTfr7K8h+anPuZ3XK+eW7nabhspg8tbG/3WNHLn0rkqf5NiFf8J3aMUH8q+UWM+jFH+EB+Qcj+j9K0qdxHddbSGzgNvMeW6rXK1HTZmW3+2L0exzi+m6/cL8nN9ttXlzThxHN0i5O1DdBtp0rqU4mEySLd3UiaIjZT+9/ohcrVOVtA9ojpI7tQm35soXoOD9LuiaYsvNGV7XrZNNch8gO4tFvMpXodQ+tCXG7l+W5wxJL1+W3yoLzegLX5I90bBfr61LW6mxApjiFy/PeobAP8RWJbbM+gWB+sH6TaoLcapu35bPHWIbvdpC4pnwi1ZlzdkOY+mjEF1vvku5c/BgpS9gxK0ueZd26MuLBdkPd2cMkso13a9dq9LmRso12NN69BMfyTzOGBInm/L8zZS+tshlEeiFmT+F1CMRIfTPYI0Qpkn96eMA2sbuWua+ruOMq9dkXWwhvLCDLjvuKLc/3hK4HUGlOO4Hb0GebB/KGuGdo20rrfd/pGti976SFz0PlsH7Gv3t8e3DJCp67g2/dZI0H7ubrarseKG5pz2DVn3DDi/rk3G07svX71Z2nz759axs3+s7h8v/foHbdOA/Vub9OazrbGu9Tbp12vrdVLbte+hUv/g3dnIru/lOZF62txLu9932v3j1cVE2uI3Od8ffx7oZyJjhfXb+T8TrafqOXR/+8bLZ0vvd/uo8Pa216DxdZDc5t7ve5p9dX6p67vWwLmB7q2Z1Xj1tdxeTlnTjVCcR3R/64u6yDPGGGOMMcYYY4wxZtLZ1WM+GWOMMcYYY4wxxpidGBufjDHGGGOMMcYYY8yUYeOTMcYYY4wxxhhjjJkybHwyxhhjjDHGGGOMMVOGjU/GGGOMMcYYY4wxZsqw8ckYY4wxxhhjjDHGTBk2PhmzEyPpDyRdl59Vkn7R/H5oT/YSSY/aUbr2kXSlpP0H7H9Aekp6lqQFkn4iae8hMtMlrRty7BxJrxkn/fdLetgE0nmnpCPHSedlki4YryzGGGOMMbsiko6R9PeTnOZZkl6f22dIetZkpm+M2TmYvqMVMMYMJyLWAPsDSDoeuDsiTmplJAlQRLzit6/h9vMb6Pk3wNcj4qOTqU/D+4EvAhvGE4qIz05R/sYYY4wxk4Kk6RGxebLTjYjTJjvNXvr/MJXpG2N2HPZ8MmYXRNLTJS2SdBpwLfA4ScslzcjjR0m6Pj2FvpT7HivpfEnXSLpa0gtz/8ckfVnSZZIWSzo69z8hvZeuy7xeNESX6ZL+W9LClHt37/i09Do6Pn8vlzSjKcOZkm6QdHH1PBqQx2HAPwHHSJqb+z6Q5y+S9K4B5+wm6XOSbpR0IbDnOPX5PuAxwBU1/dz/iazDH0p6TFNf783tZ0q6NGWu7XtkSXpB3Z/nnSnpe5KWSnpnI/fmbJPrUufdhtWrpPdlmRZIOmdYmYwxxhiz6yPpkZK+mfP+IkmHSzog1xM/To/yx6Xs5ZL+Q9L3gPe0HkV5/O78PjjP/x9Jt+R658hciyyU9LRx9Dle0rFNfrPyvFskHZT7n92sa66X9IxcCy1q0jm2rg176V8u6XlVX0knZNmvkvTYyalVY8yOwJ5Pxuy6PAs4KiKOAZBEfj8X+CDwoohYK2mPlD8FODEirkojyUXAc/LYfsCLgN8HrpX0TeBNwIURMUvSNODhQ/Q4ANgzIvbL/Gc0x6YD5wLXRsSsAefuC7wxIhZKOh94DfDVvlBEzJF0ILA6Ik7O7SOBA4FpwNW50LqxOe31wD5ZxsfnsYF36yLi05L+GTgoItZJmg48GvheRPyrpE8BRwOf6J06Gzg+Ii5Mw9luwNOzHg4CPg0cFhHLs32eCbwUmAH8VMV4+IfAaynttVnS6cARwMiQev0A8JSI2NSra2OMMcY8+HglsCIi/gpA0qOBi4FXR8QvJR0OnEBZpwDMiIiXpOxZ46T7XMoaZC2wFDgjIg6U9B7gXcB7J6jf9DzvUODDwMuAY4DPRMRXVMJETAMeiOHokcBVEXGcpBOBtwEfewDpGGN2Amx8MmbXZSQi5g/YfwjwtYhYC1C/KYuBfauRCpgpqRqULoiIDcAGSfOA5wPzgS+kUeWCiFgwRI8lme5ngG8B326OnQmcO8TwBLAkIhbm9o+BvYcXdxsOAv43In4NoBJj6cVsa3z6c2B2RGwFlku6fIJpV9ZHxMWNbge1ByXNpBiHLgTI+qtGwOcAnwNeHhGrmtMuiohNwO2S1gJ7Udrl+cA1ee7DgVuBSxhcrzcA50j6BuDYUsYYY8yDm4XASZJmUW4c3kFZZ3wn1w3TgJWN/NcmmO78iFgJIGmEbp2xEPiL7dDv/Pxu13E/BI6T9ETg/IhY3Kw/t4dNlDLX9F/+QBIxxuwc+LE7Y3Zd7hmyX0AM2X9gROyfnydExPo81pePiLgUOJiyoPmKhgTZzrhUfwRcCbwb+EJz+PvASyXtPkTXjc32FiZuEJ/oCmZQPUyUTc32MN2Gpb8iz+8HXB9UXgFfbNpl34j46Dj1+gqKB9eBFIPVtO0okzHGGGN2ISLiFoqX+ULg48DrgBuadcN+EfGXzSnt+nAz+X9PxfrTvqymXZNsbX5vZfscFOp5966VIuJc4DBgPXCJpENaXZKBoRZ6jEVEXWttzzrRGLMTYuOTMQ8+5gJH1Mftmsfu5gJtnKHWMPIaSbtL2pPi4XONpKcAqyLidOAs4I8HZSZpL0rA8/Mo7tZ/0hw+PfP9aj7KNlnMA14r6eGSfg94NXDFAJkjMn7SE4CX3E+adwETfgtfRNwBrJb01wCSHibpEXl4LfAq4MQa/2Ac5gJvyLqvbzh88qB6TUPTE9Mw+C8Uz6lHDEvYGGOMMbs2kh4P/DoizgFOAl4A7CXpT/P4QyQ9e8jpP6MYrqCslR4yxeqSOj0VWBoRpwBzKDfTbgMek+uc3SnrJGPM7xC2HhvzICMirs/n4udJ2kxxU34rxfD0eUlHUa79y+iMUfMp8QOeBHw4Im5TCTz+fkljwN2UGFCDeBJwZt5RC0q8qVafEyWdAJylSXo1b0RcLWl26g3w+Ywb1Y5pX6e4jS8CbqYYo8bjdGCupFsp8RUmwpGURxNPoHg6va7RcaVKoPRvjVfu1PvfM+/dgDFKrIQt3LdepwPnSnoU5ebBrIi4a4K6GmOMMWbXYz/gPyVtpawR3k7xIjol4z9NB06mPJbf57+Ab0i6Gvguw73mJ5vDgTflGnIV8JGIGJP0EeBHwChw029JF2PMToI6T0ZjzO8ikj5GBvLe0boYY4wxxhhjjHnw4cfujDHGGGOMMcYYY8yUYc8nY8yEkXQN931c9+8i4sZB8g8wj9OAF/Z2fyoizp6k9OcAT+7tPjYi5k5G+sYYY4wxuzqSjgP+trf7vIg4YUfoY4zZ9bHxyRhjjDHGGGOMMcZMGX7szhhjjDHGGGOMMcZMGTY+GWOMMcYYY4wxxpgpw8YnY4wxxhhjjDHGGDNl2PhkjDHGGGOMMcYYY6YMG5+MMcYYY4wxxhhjzJTx/6w+3GdZP9V1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = dataSet['serum_insulin'].mean()\n",
    "print(mean)\n",
    "### dtype 使用int 会选择所有行....0 是Flase ,为什么呢?\n",
    "miss_serum_insulin = miss_serum_insulin.astype('bool')\n",
    "dataSet.loc[miss_serum_insulin,'serum_insulin'] = mean\n",
    "\n",
    "mean = dataSet['Triceps_skin_fold_thickness'].mean()\n",
    "\n",
    "sub_serum =dataSet.loc[dataSet['Triceps_skin_fold_thickness'] == 0,'Triceps_skin_fold_thickness'] = mean\n",
    "f = plt.figure(figsize=(20,20))\n",
    "ax = plt.subplot(1,2,1)\n",
    "sns.countplot(x='Triceps_skin_fold_thickness',data=dataSet,ax=ax)\n",
    "ax = plt.subplot(1,2,2)\n",
    "sns.countplot(x='serum_insulin',data=dataSet,ax=ax)\n",
    "mean2 = dataSet['Triceps_skin_fold_thickness'].mean()\n",
    "print(mean,mean2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "score() got an unexpected keyword argument 'scoring'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-36-89c77221e602>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0mregression\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0mregression\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mregression\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'log'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: score() got an unexpected keyword argument 'scoring'"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "# from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split  \n",
    "Y = dataSet['Target']   \n",
    "X = dataSet.drop([\"Target\"], axis=1).astype('float64')\n",
    "\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "# np.isnan(Y)\n",
    "# np.isnan(X)\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size= 0.25)\n",
    "regression = LogisticRegression(solver = 'lbfgs')\n",
    "regression.fit(x_train,y_train)\n",
    "regression.coef_\n",
    "regression.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "regression.fit?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 采用5折交叉验证，分别用log似然损失和正确率，对Logistic回归模型的正则超参数调优。（各50分） "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用log似然损失调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Expected sequence or array-like, got estimator LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n          intercept_scaling=1, max_iter=100, multi_class='ovr',\n          n_jobs=None, penalty='l2', random_state=None, solver='lbfgs',\n          tol=0.0001, verbose=0, warm_start=False)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-64-c1dba8b9a084>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mlog_loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mregression\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLogisticRegressionCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlog_loss\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msolver\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'lbfgs'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'l2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mregression\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mregression\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mC_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0ms\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0mregression\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m   1797\u001b[0m                       \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1798\u001b[0m                       )\n\u001b[0;32m-> 1799\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miter_encoded_labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1800\u001b[0m             for train, test in folds)\n\u001b[1;32m   1801\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    915\u001b[0m             \u001b[0;31m# remaining jobs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 917\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    757\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    758\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 759\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    760\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    761\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    714\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    715\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 716\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    717\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    180\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    547\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    548\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 549\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 225\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 225\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py\u001b[0m in \u001b[0;36m_log_reg_scoring_path\u001b[0;34m(X, y, train, test, pos_class, Cs, scoring, fit_intercept, max_iter, tol, class_weight, verbose, solver, penalty, dual, intercept_scaling, multi_class, random_state, max_squared_sum, sample_weight)\u001b[0m\n\u001b[1;32m    992\u001b[0m             \u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog_reg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    993\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 994\u001b[0;31m             \u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscoring\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog_reg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    995\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mcoefs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_iter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    996\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py\u001b[0m in \u001b[0;36mlog_loss\u001b[0;34m(y_true, y_pred, eps, normalize, sample_weight, labels)\u001b[0m\n\u001b[1;32m   1762\u001b[0m     \"\"\"\n\u001b[1;32m   1763\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1764\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1765\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1766\u001b[0m     \u001b[0mlb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLabelBinarizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    229\u001b[0m     \"\"\"\n\u001b[1;32m    230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 231\u001b[0;31m     \u001b[0mlengths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_num_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mX\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mX\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    232\u001b[0m     \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    229\u001b[0m     \"\"\"\n\u001b[1;32m    230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 231\u001b[0;31m     \u001b[0mlengths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_num_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mX\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mX\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    232\u001b[0m     \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_num_samples\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;31m# Don't get num_samples from an ensembles length!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m         raise TypeError('Expected sequence or array-like, got '\n\u001b[0;32m--> 132\u001b[0;31m                         'estimator %s' % x)\n\u001b[0m\u001b[1;32m    133\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__len__'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'shape'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__array__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Expected sequence or array-like, got estimator LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n          intercept_scaling=1, max_iter=100, multi_class='ovr',\n          n_jobs=None, penalty='l2', random_state=None, solver='lbfgs',\n          tol=0.0001, verbose=0, warm_start=False)"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.metrics import log_loss\n",
    "regression = LogisticRegressionCV(Cs=[0.1,1,5,10,20],cv=5,scoring='neg_log_loss',solver='lbfgs',penalty='l2')\n",
    "regression.fit(x_train,y_train)\n",
    "print(regression.C_)\n",
    "s  = regression.score(x_test,y_test)\n",
    "regression.scores_\n",
    "print(type(s))\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8177083333333334"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "regression = LogisticRegression(solver = 'lbfgs')\n",
    "cv = GridSearchCV(regression,param_grid={'C':[0.1,1,10,100]},cv=5, iid= True)\n",
    "cv.fit(x_train,y_train)\n",
    "# print(cv.cv_results_)\n",
    "print(cv.best_index_)\n",
    "cv.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_loss?"
   ]
  }
 ],
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